1. Field of the Invention
The present invention relates to wireless communications, and more specifically to a method and apparatus for precoding in a wireless communication system.
2. Related Art
MIMO technology can be used to improve the efficiency of data transmission and reception using multiple transmission antennas and multiple reception antennas. MIMO technology may include a space frequency block code (SFBC), a space time block code (STBC), a cyclic delay diversity (CDD), a frequency switched transmit diversity (FSTD), a time switched transmit diversity (TSTD), a precoding vector switching (PVS), spatial multiplexing (SM) for implementing diversity. An MIMO channel matrix according to the number of reception antennas and the number of transmission antennas can be decomposed into a number of independent channels. Each of the independent channels is called a layer or stream. The number of layers is called a rank.
The dirty paper coding scheme, one of MIMO technology, can reduce interference by removing other user's data which act as interference in advance from a transmitting unit, and is known to provide, in theory, maximum channel capacities in MIMO system. Reference can be made to M. H. M. Costa, “Writing on Dirty Paper,” IEEE Trans. Inf. Theory, vol. 29, no. 3, pp. 439-441, May 1983, for the technology. The dirty paper coding scheme, however, is difficult to implement in practical system since the scheme requires lots of channel information and presents much complexity in calculation due to the nature of non-linear calculation. Therefore, various alternative methods that can implement the principle of dirty paper coding scheme by using linear calculation have been proposed. Among various alternative methods, a zero-forcing beamforming (ZF-BF) method uses the pseudo inverse matrix of a channel as a precoding matrix of a base station, taking advantage of the property that unit matrix is obtained by multiplying the channel with the precoding matrix. In other words, user pre-processing is carried out in the base station taking consideration of the interference between users. The ZF-BF method has the merit of relatively low complexity in calculation and ease of actual implementation through the use of only linear calculations.
To implement the ZF-BF method in real application, it is necessary to meet the condition of power constraint per base station. A method of power allocation maximizing sum rate while meeting the condition of power constraint per base station can be proposed. Also, power constraint per antenna or per antenna group including multiple antennas can be considered for implementing actual systems. As for the method considering per antenna power constraint, a pseudo-inverse with optimal power allocation (Pinv-Opt-PA) method allocates power so that sum rate is maximized while every antenna meets the condition of power constraint at the pseudo inverse matrix of the channel. For this method, reference can be made to F. Boccardi and H. Huang, “Optimum power allocation for the MIMO-BC zero-forcing precoder with per-antenna power constraints,” in roc. Conf. Information Sciences Systems (CISS), March 2006. The Pinv-opt-PA method can be formularized to the problem of convex optimization which is widely known in optimization theory. As an alternative, a generalized inverse with optimal power allocation (Ginv-Opt-PA) method which allocates power so that sum rate is maximized while every antenna meets the power constraint at the generalized inverse matrix of the channel can be applied. For this method, reference can be made to Ami Wiesel, Yonina C. Eldar and Shlomo Shamai (Shitz), “Zero-Forcing Precoding and Generalized Inverses,” IEEE Trans. Signal Process., vol. 56, no. 9, pp. 4409-4418, September 2008. The generalized inverse matrix of matrix A can be represented as the sum of pseudo inverse matrix of A and the matrix belonging to the null space of A, in which the generalized inverse matrix presents inverse matrix with degree of more freedom compared to the pseudo inverse matrix. The Ginv-Opt-PA method seeks optimum inverse matrix which can be easily applied for power allocation through generalized inverse matrix, and thus is known to be the optimal ZF precoding method considering per antenna power constraint. The Ginv-opt-PA method, however, relates to the problem of non-convex optimization in which solution can exist in special cases. Therefore, the method presents difficulty in implementing for actual systems.
Therefore, it is necessary to provide a method of precoding that can be easily implemented in real systems by reducing the amount of calculation while minimizing the difference in performance from conventional precoding methods.